Thousands of artificial intelligence (AI) developers around the world have been getting behind the virtual wheel of Amazon’s AWS DeepRacer, a 3D, cloud-based racing simulator, and pushing the limits of machine learning. The advanced ML technique of reinforcement learning (RL), specifically, will be put to the test when Amazon releases its 1/18th scale DeepRacer Evo autonomous vehicle later this year. The ambitious project is intended to accelerate advancements in object avoidance and other innovations required for the real-world deployment of driverless cars.

We’ve heard about the promise of autonomous vehicles for so long now that it seems inevitable. A major driving force behind self-driving vehicles is AI, machine learning in particular, and Amazon’s AWS Machine Learning tools and services are providing a path forward. The following is an overview of AWS Machine Learning, its various services and tools, its advantages, and a basic review of Amazon Web Services.

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What is AWS Machine Learning?

The goal of machine learning, a subset of AI, is to train machines on how to properly respond to their surroundings (via data inputs) and “learn” without direct programming. Amazon’s AWS ML offerings include tools and services to help organizations across the entire ML spectrum. Among other capabilities, they help developers: 

  • Build, train, and deploy machine learning models
  • Apply reinforcement learning to the training of complex sequences of behaviors in a dynamic environment
  • Create recommendation engines to serve their customers better
  • Fine-tune forecasting models to help businesses make better, data-backed decisions
  • Enhance computer vision, which allows machines to quickly and accurately identify people and objects in images

Some of the more well-known businesses using AWS Machine Learning include Netflix, CapitalOne, BMW, and the National Football League. Amazon’s offerings include pre-trained AI models, which are useful for forecasting, recommendations, computer vision, and language, and Amazon SageMaker to help organizations build and train their models. In addition to the AWS DeepRacer project mentioned above, developers are using AWS Machine Learning to efficiently test thousands of potential product designs, make quick and accurate property damage assessments following natural disasters, improve health care outcomes, enhance customer service responses, and much more. 

The machine learning market is predicted to increase at a compound annual growth rate (CAGR) of 42.8 percent from 2018 to 2024 (becoming a $30.6 billion market), according to a roundup of machine learning market predictions published by Forbes. This phenomenal growth in machine learning (and AI in general) will do doubt encourage more professionals to embark on a path toward becoming an AI or ML engineer. At the same time, organizations may struggle to fill those roles as this technology provides competitive advantages. Learning how to leverage AWS Machine Learning tools and services is smart business for both professionals and organizations.

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Overview of Amazon Web Services

Amazon Web Services, or only AWS, is Amazon’s cloud services platform, which provides flexibility and scalability for organizations of all sizes to deploy services and manage data. Instead of deploying physical servers, AWS allows companies to use (and pay for) only the database storage, compute power, content delivery, and on-demand AWS services (such as AWS Machine Learning) they need. Competitors include Microsoft Azure and Google Cloud.

AWS allows organizations to tap into a growing set of services and capabilities without having to build it in house, which saves money and speeds up deployment times. Some of the reasons companies prefer AWS to other cloud services include the following:

  • Security - Data is encrypted to provide end-to-end security
  • Experience - Amazon was an early pioneer of cloud computing and can draw from its years of experience to provide best-in-class solutions
  • Flexibility - AWS offers exceptional flexibility and, for instance, allows developers to select the operating system language and database 
  • Usability - Developers consider AWS to be relatively easy to use, as they can quickly deploy applications, build new apps, or migrate existing ones 
  • Scalability - Depending on user requirements, developers can scale up or down as needed

What are AWS Machine Learning Services and Tools?

Amazon offers several services and tools under the AWS Machine Learning umbrella. These solutions enable developers and organizations to more quickly deploy their ML systems as compared to a code-based approach. Keep in mind that the terms “tools” and “services” are often interchanged when discussing AWS Machine Learning solutions. The following is a brief explanation of each.

1. SageMaker

This managed service is designed to help you quickly and efficiently transition your conceptual machine learning models into production. SageMaker includes several tools that enable you to design, build, and deploy your ML model and has an “autopilot” feature that will automatically run your model through multiple algorithms to determine which is the most effective.

2. Comprehend

This natural language processing (NLP) service uses machine learning to extract useful information from textual data, including unstructured data such as customer reviews and customer service emails. Since Comprehend is a fully managed service, you can use pre-trained models. 

3. Fraud Detector

As its name implies, Amazon Fraud Detector is designed to flag potentially fraudulent accounts. Organizations must enter existing data of known fraudulent transactions to train it for future use.

4. Lex

Lex allows you to build conversational chatbots for use in customer service, sales, and other such applications. Lex offers a natural language understanding (NLU) component that can make sense of conversational language and offer the correct feedback.

5. Translate

Similar to Google Translate, Amazon Translate is a neural machine translation service that allows you to localize sites for different regions and translate large volumes of text. This service also allows you to customize to take brand names and unique jargon into account.

6. Rekognition

This is a computer vision service that streamlines the development process for applications that can recognize specific people and objects from images (including video). Rekognition allows organizations to customize per business needs.

7. CodeGuru

This service helps developers spot potential problems with their code before it’s too late. For example, CodeGuru can recognize leaks or inefficiencies with CPU cycles and then suggest solutions based on the context of the code itself.

8. Forecast

This service uses existing datasets to provide time-series predictions for organizations. For instance, Forecast can be used to predict business expenses, customer support, even future stock prices.

9. DeepRacer

As discussed above, DeepRacer is a 3D virtualization of an automobile with a corresponding 1/18-scale model car that allows driverless automobile developers to test their AI algorithms. Developers can even compete against other developers on virtual racetracks.

10. Kendra

This hosted service is an enterprise search engine optimized to help customers with product queries. Kendra also understands natural language questions, which can help organizations save money in customer support.

11. Personalize

Amazon’s Personalize function helps you gauge your customers’ usage patterns and then make recommendations based on those patterns. Personalizing the user experience helps drive sales and maintain loyal customers.

12. Polly

This tool is used for creating speech-enabled products that mimic conversational styles across a range of languages. Polly can “read” written text and convert it into conversational speech.

13. Textract

Textract automatically extracts information from scanned documents, dramatically cutting down the time and resources needed to digitize documents. Textract can identify tables and other layout features that are important to the context of written documents.

14. DeepLens

A deep-learning enabled camera, DeepLens is designed to help develop and test computer vision models. This helps speed the deployment process by allowing developers to test their models in real-time.

Advantages of AWS Machine Learning

Amazon’s AWS Machine Learning suite of services can help cut down the time and expense it typically takes to develop, test, and deploy ML models. For instance, adding specifics to pre-trained models can help a company quickly deploy a chatbot to help with customer service tasks. AWS also supports all of the major machine learning frameworks, such as TensorFlow and Caffe2. 

It’s also secure, with end-to-end encryption, and provides a “pay-as-you-go” model that allows organizations of all sizes to scale as needed. Also, AWS provides numerous data analysis services to help make the best business decisions possible. A known leader in cloud computing, Amazon offers a fantastic end-to-end solution for companies implementing machine learning into their products, services, and operations.

Interested in beginning your cloud career? Try answering these Professional Cloud Architect Practice Exam Questions and assess your understanfing of the various cloud concepts.

Get in the Fast Lane: Become Certified as an AWS Cloud Architect

As you can see, Amazon’s AWS cloud computing platform is arguably the best in its class, prompting businesses of all sizes to take notice. As a certified AWS Cloud Architect, you’ll be best positioned to harness the power of Amazon’s AWS Machine Learning suite of services and many others. Simplilearn’s comprehensive AWS Cloud Architect Master’s Program will help you become career-ready in this highly coveted skillset. Best of all, you can learn on your own time and from the comfort of your own home. Get started today! 

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